{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Decision Trees\n",
    "Let's start with a large dataset and find out how we can use decision trees when our data size is large-sh. We will use the kaggle dataset that attempts to classify plankton. We will use some example code to get us started from the tutorial here:\n",
    "http://www.kaggle.com/c/datasciencebowl/details/tutorial\n",
    "\n",
    "*For questions on this notebook, please contact Professor Eric Larson at eclarson@smu.edu*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of classes: 121\n"
     ]
    }
   ],
   "source": [
    "# load another dataset (large) and train using various methods of gradient (and mini-batch)\n",
    "import glob\n",
    "import os\n",
    "\n",
    "# change this to point to the dataset on your machine/cluster!!\n",
    "directory_of_dataset = \"/Users/eclarson/Desktop/\"\n",
    "\n",
    "# get the classnames from the directory structure\n",
    "directory_names = list(set(glob.glob(os.path.join(directory_of_dataset,\"kaggle_plank\", \"*\"))\n",
    " ).difference(set(glob.glob(os.path.join(directory_of_dataset,\"kaggle_plank\",\"*.*\")))))\n",
    "\n",
    "print ('number of classes:', len(directory_names))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading images\n",
      "5.0 % done\n",
      "10.0 % done\n",
      "15.0 % done\n",
      "20.0 % done\n",
      "25.0 % done\n",
      "30.0 % done\n",
      "35.0 % done\n",
      "40.0 % done\n",
      "45.0 % done\n",
      "50.0 % done\n",
      "55.0 % done\n",
      "60.0 % done\n",
      "65.0 % done\n",
      "70.0 % done\n",
      "75.0 % done\n",
      "80.0 % done\n",
      "85.0 % done\n",
      "90.0 % done\n",
      "95.0 % done\n",
      "100.0 % done\n"
     ]
    }
   ],
   "source": [
    "# Rescale the images and create the combined metrics and training labels\n",
    "from skimage.transform import resize\n",
    "from skimage.io import imread\n",
    "import numpy as np\n",
    "\n",
    "#get the total training images\n",
    "numberofImages = 0\n",
    "for folder in directory_names:\n",
    "    for fileNameDir in os.walk(folder):   \n",
    "        for fileName in fileNameDir[2]:\n",
    "             # Only read in the images\n",
    "            if fileName[-4:] != \".jpg\":\n",
    "              continue\n",
    "            numberofImages += 1\n",
    "\n",
    "# We'll rescale the images to be 40x40\n",
    "maxPixel = 25\n",
    "imageSize = maxPixel * maxPixel\n",
    "num_rows = numberofImages # one row for each image in the training dataset\n",
    "num_features = imageSize # for our ratio\n",
    "\n",
    "# X is the feature vector with one row of features per image\n",
    "# consisting of the pixel values and our metric\n",
    "X = np.zeros((num_rows, num_features), dtype=float)\n",
    "# y is the numeric class label \n",
    "y = np.zeros((num_rows))\n",
    "\n",
    "files = []\n",
    "# Generate training data\n",
    "i = 0    \n",
    "label = 0\n",
    "# List of string of class names\n",
    "namesClasses = list()\n",
    "\n",
    "print (\"Reading images\")\n",
    "# Navigate through the list of directories\n",
    "for folder in directory_names:\n",
    "    # Append the string class name for each class\n",
    "    currentClass = folder.split(os.pathsep)[-1]\n",
    "    namesClasses.append(currentClass)\n",
    "    for fileNameDir in os.walk(folder):   \n",
    "        for fileName in fileNameDir[2]:\n",
    "            # Only read in the images\n",
    "            if fileName[-4:] != \".jpg\":\n",
    "              continue\n",
    "            \n",
    "            # Read in the images and create the features\n",
    "            nameFileImage = \"{0}{1}{2}\".format(fileNameDir[0], os.sep, fileName)            \n",
    "            image = imread(nameFileImage, as_grey=True)\n",
    "            files.append(nameFileImage)\n",
    "            #axisratio = getMinorMajorRatio(image)\n",
    "            image = resize(image, (maxPixel, maxPixel))\n",
    "            \n",
    "            # Store the rescaled image pixels and the axis ratio\n",
    "            X[i, 0:imageSize] = np.reshape(image, (1, imageSize))\n",
    "            #X[i, imageSize] = axisratio\n",
    "            \n",
    "            # Store the classlabel\n",
    "            y[i] = label\n",
    "            i += 1\n",
    "            # report progress for each 5% done  \n",
    "            report = [int((j+1)*num_rows/20.) for j in range(20)]\n",
    "            if i in report: print (np.ceil(i *100.0 / num_rows), \"% done\")\n",
    "    label += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "num features: (30336, 625)\n",
      "num labels: (30336,)\n"
     ]
    }
   ],
   "source": [
    "# here is where the online tutorial code stops and my code starts\n",
    "print ('num features:', X.shape)\n",
    "print ('num labels:', y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "StratifiedShuffleSplit(n_splits=1, random_state=None, test_size=0.1,\n",
      "            train_size=0.5)\n"
     ]
    }
   ],
   "source": [
    "# now divide the data into test and train using scikit learn built-ins\n",
    "from sklearn.model_selection import StratifiedShuffleSplit \n",
    "\n",
    "cv = StratifiedShuffleSplit(n_splits=1,train_size=0.5)\n",
    "print (cv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Once:\n",
      "CPU times: user 18.3 s, sys: 97.7 ms, total: 18.4 s\n",
      "Wall time: 18.5 s\n",
      "accuracy: 0.229400131839\n"
     ]
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn import metrics as mt\n",
    "\n",
    "dt_clf = DecisionTreeClassifier()\n",
    "\n",
    "# now get the training and testing\n",
    "for train, test in cv.split(X,y):\n",
    "    print ('Training Once:')\n",
    "    # train the decision tree algorithm\n",
    "    %time dt_clf.fit(X[train],y[train])\n",
    "    yhat = dt_clf.predict(X[test])\n",
    "    print ('accuracy:', mt.accuracy_score(y[test],yhat))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# from the tutorial: http://www.kaggle.com/c/datasciencebowl/details/tutorial \n",
    "def multiclass_log_loss(y_true, y_pred, eps=1e-15):\n",
    "    \"\"\"Multi class version of Logarithmic Loss metric.\n",
    "    https://www.kaggle.com/wiki/MultiClassLogLoss\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    y_true : array, shape = [n_samples]\n",
    "            true class, integers in [0, n_classes - 1)\n",
    "    y_pred : array, shape = [n_samples, n_classes]\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    loss : float\n",
    "    \"\"\"\n",
    "    predictions = np.clip(y_pred, eps, 1 - eps)\n",
    "\n",
    "    # normalize row sums to 1\n",
    "    predictions /= predictions.sum(axis=1)[:, np.newaxis]\n",
    "\n",
    "    actual = np.zeros(y_pred.shape)\n",
    "    n_samples = actual.shape[0]\n",
    "    actual[np.arange(n_samples), y_true.astype(int)] = 1\n",
    "    vectsum = np.sum(actual * np.log(predictions))\n",
    "    loss = -1.0 / n_samples * vectsum\n",
    "    return loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.240820093612\n",
      "Log Loss: 26.2211450302\n"
     ]
    }
   ],
   "source": [
    "yhat = dt_clf.predict(X[test])\n",
    "class_probabilities = dt_clf.predict_proba(X[test])\n",
    "print ('Accuracy:', mt.accuracy_score(y[test],yhat))\n",
    "print ('Log Loss:', multiclass_log_loss(y[test], class_probabilities))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Once:\n",
      "Accuracy: 0.29334212261\n",
      "Log Loss: 24.4070984149\n"
     ]
    }
   ],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "\n",
    "n_components = 50\n",
    "pca = PCA(n_components=n_components, svd_solver='randomized')\n",
    "\n",
    "for train, test in cv.split(X,y):\n",
    "    print ('Training Once:')\n",
    "    \n",
    "    # transform the data using pca\n",
    "    pca.fit(X[train])\n",
    "    X_train = pca.transform(X[train])\n",
    "    X_test = pca.transform(X[test])\n",
    "    \n",
    "    # train the decision tree algorithm\n",
    "    dt_clf.fit(X_train,y[train])\n",
    "    yhat = dt_clf.predict(X_test)\n",
    "    class_probabilities = dt_clf.predict_proba(X_test)\n",
    "    \n",
    "    print ('Accuracy:', mt.accuracy_score(y[test],yhat))\n",
    "    print ('Log Loss:', multiclass_log_loss(y[test], class_probabilities))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "log_losses = []\n",
    "accuracies = []\n",
    "params = []\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Once:\n",
      "Accuracy: 0.213909030982\n",
      "Log Loss: 3.66574301303\n"
     ]
    },
    {
     "data": {
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RJEmSJKlTLIIkSZIkdcpARVCSE5JckWR9u305ye9upv/BSTb2bXclud/8Q5ckSZKkwQ26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\nYxBbv+OBi6rq+oWMbR7xXQz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      "text/plain": [
       "<matplotlib.figure.Figure at 0x119548588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# how to make it generalize a bit better? \n",
    "\n",
    "# defaults\n",
    "dt_clf = DecisionTreeClassifier(criterion='gini', max_depth=None, \n",
    "                                min_samples_split=2, min_samples_leaf=1, \n",
    "                                max_features=None,\n",
    "                                max_leaf_nodes=None)\n",
    "\n",
    "# overwrite with prepruning methods\n",
    "dt_clf = DecisionTreeClassifier(criterion='entropy', max_depth=5, \n",
    "                                min_samples_split=1000, min_samples_leaf=1, \n",
    "                                max_leaf_nodes=None)\n",
    "\n",
    "for train, test in cv.split(X,y):\n",
    "    print ('Training Once:')\n",
    "    \n",
    "    # transform the data using pca\n",
    "    pca.fit(X[train])\n",
    "    X_train = pca.transform(X[train])\n",
    "    X_test = pca.transform(X[test])\n",
    "    \n",
    "    # train the decision tree algorithm\n",
    "    dt_clf.fit(X_train,y[train])\n",
    "    yhat = dt_clf.predict(X_test)\n",
    "    class_probabilities = dt_clf.predict_proba(X_test)\n",
    "    \n",
    "    # get accuracy and log loss of this training\n",
    "    acc = mt.accuracy_score(y[test],yhat)\n",
    "    ll = multiclass_log_loss(y[test], class_probabilities)\n",
    "    print ('Accuracy:', acc)\n",
    "    print ('Log Loss:', ll)\n",
    "    \n",
    "    # save accuracy, log loss, and params of run\n",
    "    log_losses.append(ll)\n",
    "    accuracies.append(acc)\n",
    "    params.append(dt_clf.get_params())\n",
    "    \n",
    "plt.figure(figsize=(10,4))\n",
    "plt.subplot(1,2,1)\n",
    "plt.bar(range(len(log_losses)),log_losses)\n",
    "plt.title('Log Losses')\n",
    "plt.xlabel('Runs')\n",
    "\n",
    "plt.subplot(1,2,2)\n",
    "plt.bar(range(len(accuracies)),accuracies)\n",
    "plt.title('Accuracies')\n",
    "plt.xlabel('Runs')\n",
    "plt.ylim([0,1])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Log Loss of \"Chance\": 4.7957905456\n"
     ]
    }
   ],
   "source": [
    "# a telling example for log loss minimization\n",
    "class_probabilities = np.ones((len(y[test]),len(np.unique(y))))\n",
    "print ('Log Loss of \"Chance\":', multiclass_log_loss(y[test], class_probabilities))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature Importances\n",
    "The feature importances can be calculated using the Mean decrease in the gini index. Essentially, every time a feature is tested for splitting, the decrease in the gini (or entropy) is saved. The importance of the feature can then be approximated by taking the mean decrease in the gini for all the nodes where the feature is tested (usually the feature is tested at every node):\n",
    "$$ Imp_{A} = \\frac{1}{|T|}\\sum_{t \\in T} gini_A(p_t) - gini_A(t) $$\n",
    "where $|T|$ is the total number of nodes in the tree, $T$ is the set of all nodes, $gini_A(t)$ is the gini for the $A^{th}$ attribute at node $t$, and $gini_A(p_t)$ is the gini for the $A^{th}$ attribute at the **parent** node for $t$.\n",
    "\n",
    "In this way, we have the mean decrease in the gini for attribute $A$. We can make a vector of these importance for all attributes in the dataset: $[Imp_1, Imp_2, ... Imp_{A_{tot}}]$.\n",
    "\n",
    "`scikit-learn` computes this value for you and it is stored inside the tree object as `dt_clf.feature_importances_`. For the current dataset, the images are feature vectors of size 25x25=625 pixels. Therefore we can look at the importances as a bar graph with 625 instances or we can reshape the instances to see the importances as a 25x25 image. Both are done below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Once:\n",
      "accuracy: 0.23005932762\n"
     ]
    },
    {
     "data": {
      "image/png": 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OM3yoLeVfX8PDowfkZ599lsq70062xx9/vKWBwSq/bE499X11B9hatWpVEwfd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\nk+wbeQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x118eb8390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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EJUb4HWfZMepBiqVCW+Ap4NYHHnLCtpIhh7fjQXZDGd90htSshQklYRlv2YL42a0FWdre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\nRtqkdcEYDUtkyLFeYY3NEbafHAln85R5a2EeRg2QkS4ILY7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      "text/plain": [
       "<matplotlib.figure.Figure at 0x118eb8978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dt_clf = DecisionTreeClassifier()\n",
    "\n",
    "# now get the training and testing\n",
    "for train, test in cv.split(X,y):\n",
    "    print ('Training Once:')\n",
    "    # train the decision tree algorithm\n",
    "    dt_clf.fit(X[train],y[train])\n",
    "    yhat = dt_clf.predict(X[test])\n",
    "    print ('accuracy:', mt.accuracy_score(y[test],yhat))\n",
    "    \n",
    "# get the importances\n",
    "imp = dt_clf.feature_importances_\n",
    "\n",
    "#plot the importances as bar chart\n",
    "plt.bar(range(len(imp)),imp)\n",
    "\n",
    "# reshape the importances to be size of the original images\n",
    "# and show it\n",
    "imp_as_image = np.reshape(imp,(25,25))\n",
    "plt.figure()\n",
    "plt.imshow((imp_as_image), cmap=plt.cm.gray)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/eclarson/anaconda/envs/MLEnv/lib/python3.5/site-packages/ipykernel/__main__.py:5: RuntimeWarning: divide by zero encountered in log\n"
     ]
    },
    {
     "data": {
      "image/png": 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IO88Lj73HOCzrmuOylm7ib0hm6cHP8X/pqXW7YPUlbZ+4nJx/F5GQ0FrWY2uT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yAAAg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\nDkRYiTi8m0kttIfMTuEwCDol+dlezHREt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PHYssv1bfuIj/UoR2jpvRWhNdWw31f71r7Y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\nhBBi55CoJYQQQgghhBBCCCF2jv8PIC4AQp9SOYIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11990a048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from random import randint\n",
    "# look at importances on a log scale\n",
    "plt.figure(figsize=(15,20))\n",
    "plt.subplot(1,4,1)\n",
    "plt.imshow(np.log(imp_as_image), cmap=plt.cm.gray)\n",
    "plt.title('Importances')\n",
    "\n",
    "for i in range(2,5):\n",
    "    plt.subplot(1,4,i)\n",
    "    plt.imshow(np.reshape(X[randint(0,len(X))],(25,25)),cmap=plt.cm.gray)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Other Methods for Making the Tree Generalize\n",
    "Everything that sciki-learn implements is used for pre-pruning the tree: that is, we stop making nodes during the decision tree induction process. However, there are other methods that post-prune the tree where:\n",
    "- We build a tree completely\n",
    "- Then select a group a leaf nodes that belong to a split \n",
    "- And, finally decide if we should prune the split (making the parent node a leaf node)\n",
    "- Then repeat until some criteria is met\n",
    "\n",
    "This is called post pruning and can be a good method for creating a decision tree that is not \"overlearned\". Let's look at these in lecture now."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:MLEnv]",
   "language": "python",
   "name": "conda-env-MLEnv-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
